• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.4
• /
• pp.76-85
• /
• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.433-442
• /
• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Journal of Digital Contents Society
• /
• v.1 no.1
• /
• pp.53-64
• /
• 2000

This paper is related to color image segmentation scheme which makes it possible to achieve the excellent segmented results by block-based segmentation using Y/C bit-plane summation image. First, normalized chrominance summation image is obtained by normalizing the image which is summed up the absolutes of color-differential values between R, G, B images. Secondly, upper 2 bits of the luminance image and upper 6bits of and the normalized chrominance summation image are bitwise operated by the pixel to generate the Y/C bit-plane summation image. Next, the Y/C bit-plane summation image divided into predetermined block size, is classified into monotone blocks, texture blocks and edge blocks, and then each classified block is merged to the regions including one more blocks in the individual block type, and each region is selectively allocated to unique marker according to predetermined marker allocation rules. Finally, fine segmented results are obtained by applying the watershed algorithm to each pixel in the unmarked blocks. As shown in computer simulation, the main advantage of the proposed method is that it suppresses the over-segmentation in the texture regions and reduces computational load. Furthermore, it is able to apply global parameters to various images with different pixel distribution properties because they are nonsensitive for pixel distribution. Especially, the proposed method offers reasonable segmentation results in edge areas with lower contrast owing to the regional characteristics of the color components reflected in the Y/C bit-plane summation image.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.04b
• /
• pp.499-501
• /
• 2000

본 논문에서는 영역기반 검색환경을 제공하는 FRIP(Finding Region in the Pictures) 시스템을 소개한다. FRIP 시스템은 영역 기반 검색환경을 제공하기 위해서, 우선적으로 영상을 분할하고, 각 분할된 영역으로부터 색상, 질감, 크기, 모양, 위치 정보와 같은 최적의 특징 벡터들을 추출하여 색인화시킨다. 그런 뒤에, 사용자가 검색하고자 하는 영역과 검색 영상 수 k를 입력하면, 유사성 측정 식에 의해 가장 유사한 k만큼의 영상을 우선 순위 형태로 사용자에 보여주게 된다. 본 시스템에서는 영상을 분할하기 위해서 기본적인 RGB 색상계를 확장(Scaling 및 이동(Shifting) 알고리즘을 통해 영상의 대비 정도가 향상된 새로운 색상계로 변환시키고, 원형 필터를 설계하여, 영역 안에 포함된 의미 없는 작은 영역을 제거하도록 하였다. 그리고 이렇게 분할된 각 영역들로부터, 본 시스템에서 제안하는 모양 기술자인 MRS(Modified Radius-based Signature)를 포함하여 5가지의 최적의 특징 벡터들을 전처리 단계에서 데이터베이스에 색인으로 저장하고 유사성 측정을 위한 수치로 사용하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.04a
• /
• pp.815-818
• /
• 2014

영상 분석을 통한 특징 추출은 객체의 인식이나 매칭, 인덱싱 등을 위해 수반되는 준비 단계로서 분야별로 다양한 방식을 통해 수행되어 왔다. 특히, 피부 영상 분석에 있어 주목할 만한 이슈는 피부의 노화 정도를 측정하는 것이다. 피부의 거칠기는 피부의 상태와 노화를 판단하는 중요한 근거의 하나이다. 본 논문에서는 피부 나이를 측정하기 위해 피부 현미경 영상에서 피부 거칠기를 평가하는 방법을 제안한다. 이를 위해 피부 현미경으로 촬영된 이미지에 이진화 및 질감 대비 향상, 노이즈 제거 등의 전처리 과정을 수행하고, Watershed 알고리즘과 외곽선 검출을 통해, 피부를 구성하는 셀들의 영역 정보를 획득한다. 이를 바탕으로 피부 거침의 변화량을 계산하여 거칠기를 정의한다. 제안한 방법의 효과를 검증하기 위해 다양한 연령대의 피험자로부터 피부 현미경 영상을 확보하고 실험을 통해 피부 거칠기 특징이 피험자의 연령대와 상관관계가 있음을 보인다.

• Journal of Advanced Navigation Technology
• /
• v.22 no.2
• /
• pp.163-167
• /
• 2018

Probabilistic approach is applied to the experiment of Probability Density Function to get the information. Especially this method will be useful to make the montage to compare similarity. But in the case of art painting, it is more difficult than montage image. In this case, we should study the habit of painter with characteristic point in the paintings. Especially we will study characteristic point in the oil paintings to decide truth or falsehood in this paper.

• Journal of the Korean Society of Radiology
• /
• v.7 no.1
• /
• pp.9-15
• /
• 2013

In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was $40{\times}40$ pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.

• Journal of the Institute of Convergence Signal Processing
• /
• v.2 no.4
• /
• pp.22-30
• /
• 2001

In this paper we propose three classification algorithms to classify breast tumors that occur in duct into Benign, DCIS(ductal carcinoma in situ) NOS(invasive ductal carcinoma) The general approach for a creating classifier is composed of 2 steps: feature extraction and classification Above all feature extraction for a good classifier is very significance, because the classification performance depends on the extracted features, Therefore in the feature extraction step, we extracted morphology features describing the size of nuclei and texture features The internal structures of the tumor are reflected from wavelet transformed images with 10$\times$ and 40$\times$ magnification. Pariticulary to find the correlation between correct classification rates and wavelet depths we applied 1, 2, 3 and 4-level wavelet transforms to the images and extracted texture feature from the transformed images The morphology features used are area, perimeter, width of X axis width of Y axis and circularity The texture features used are entropy energy contrast and homogeneity. In the classification step, we created three classifiers from each of extracted features using discriminant analysis The first classifier was made by morphology features. The second and the third classifiers were made by texture features of wavelet transformed images with 10$\times$ and 40$\times$ magnification. Finally we analyzed and compared the correct classification rate of the three classifiers. In this study, we found that the best classifier was made by texture features of 3-level wavelet transformed images.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1037-1052
• /
• 2019

We had developed in preceding study a classification model for the Korean pine and Larch with an accuracy of 98 percent using Hyperion and Sentinel-2 satellite images, texture information, and geometric information as the first step for tree species mapping in the inaccessible North Korea. Considering a share of major tree species in North Korea, the classification model needs to be expanded as it has a large share of Oak(29.5%), Pine (12.7%), Fir (8.2%), and as well as Larch (17.5%) and Korean pine (5.8%). In order to classify 5 major tree species, national forest type map of South Korea was used to build 11,039 training and 2,330 validation data. Sentinel-2 data was used to derive spectral information, and PlanetScope data was used to generate texture information. Geometric information was built from SRTM DEM data. As a machine learning algorithm, Random forest was used. As a result, the overall accuracy of classification was 80% with 0.80 kappa statistics. Based on the training data and the classification model constructed through this study, we will extend the application to Mt. Baekdu and North and South Goseong areas to confirm the applicability of tree species classification on the Korean Peninsula.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.291-304
• /
• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.